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		<title>Surgical AI Learns to Adapt When Smoke and Darkness Blur the Robot&#8217;s View</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[batch normalization]]></category>
		<category><![CDATA[domain shift]]></category>
		<category><![CDATA[endoscopic vision]]></category>
		<category><![CDATA[EndoVis 2017]]></category>
		<category><![CDATA[instrument segmentation]]></category>
		<category><![CDATA[low illumination]]></category>
		<category><![CDATA[optical flow]]></category>
		<category><![CDATA[predictive entropy]]></category>
		<category><![CDATA[robot-assisted surgery]]></category>
		<category><![CDATA[surgical robotics**]]></category>
		<category><![CDATA[test-time adaptation]]></category>
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					<description><![CDATA[A new test-time adaptation framework shows that online model updates can rescue surgical instrument segmentation from severe low light and blur, but normalization statistics, not temporal machinery, drive most of the recovery.]]></description>
		
		
		
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